A Statisticians Perspective on Biomarkers in Drug Development

A Statistician’s Perspective on Biomarkers in Drug Development PSI Biomarkers Special Interest Group Martin Jenkins and Chris Harbron, Astra. Zeneca Co-authors: Aiden Flynn, Trevor Smart, Chris Harbron, Tony Sabin, Jayantha Ratnayake, Paul Delmar, Athula Herath, Philip Jarvis and James Matcham; Volume 10, Issue 6, Pages 494 -507, November/December 2011

What is a biomarker? Biological marker (biomarker): A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. NIH biomarker definitions working group, 2001 • Many endpoints could be considered as biomarkers • Commonly thought of in terms of biological / tissue samples (eg blood), imaging techniques or even examinations • Many technology platforms (proteomics, histopathology etc) • Does not define one purpose and so a clear objective and terminology can be helpful • Appear at many stages throughout the development program 27/09/2012 PSI Biomarker SIG 2

Types of biomarker 27/09/2012 PSI Biomarker SIG 3

Biomarker Examples Biomarker Current Use Classification HER 2, EGFR, K-RAS mutations Directing treatment in oncology Predictive biomarker Defines indication in label, diagnostic development UGT 1 A 1, TMPT polymorphisms Predisposition to certain toxicities Predictive biomarker Appear in label as risk factor suggesting dose adjustment AB 1 -42 Diagnose prodromal Alzheimer’s Disease Prognostic biomarker Enrich trial populations. Example of qualification Gene signature chips Prognosis prediction in oncology Prognostic biomarker Diagnostic qualification process applies CRP, IL-6, TNFa in blood samples Proof of principle in inflammatory diseases Pharmacodynamic biomarker Fit for purpose assay validation FDG-PET imaging Proof of concept (eg Tumour metabolism) Pharmacodynamic biomarker Collaborative opportunities for qualification Hb. A 1 c Represents glycemic control in diabetics Surrogate Endpoint Primary evidence for label. Qualification burden 27/09/2012 PSI Biomarker SIG Qualification 4

What characteristics are we interested in? • It is generally advisable to learn as much as possible about a biomarker prior to it’s application for decision-making – The science behind the choice of biomarker should be supported prior to initiating studies – The practical feasibility of using the marker should be examined – The statistical properties of the biomarker are of interest • Statistical properties of interest include – Variability estimates (and components of variability, within and between subjects) – Effect sizes (using a positive control, challenge model or other data sources) and dynamic range – Distributions and scaling approaches required – Appropriate analysis methods considered 27/09/2012 PSI Biomarker SIG 5

Methodology studies • Feasibility / Methodology studies can be used to learn the characteristics of an endpoint, but also the practicalities – Feasibility of multiple assessments – Acceptability to the patient, degree of dropout • The same design and analysis methodology should be used as is envisaged for the subsequent clinical trial • Information learned can aid in designing or sizing this trial • Work carried out should be fit-for-purpose enough to ensure that there is sufficient confidence that decisions can be made based upon the biomarker, given it’s intended use. Further studies, reviews or meta-analyses can aid in this if required. 27/09/2012 PSI Biomarker SIG 6

Sources of variation Much biomarker work is accompanied by similar practical challenges around controlling sources of variation: • Patient-related factors • Individual biological factors: genetics, race, age, comorbidities • Social and habitual factors: smoking, physical fitness, diet • Natural biological variation: eg diurnal effects • External factors: • Pre-analytical variation: sample collection and fixation practices • Analytical variation: between laboratories, readers and batches, technical precision of the assay 27/09/2012 PSI Biomarker SIG 7

Sources of bias Bias can arise, especially in open-label studies, for several reasons: • Verification bias because of the choice of locally available methods • Patient selection bias, for example, patients with breast cancer family history, may not consent to BRCA DNA testing • Treatment allocation bias, if treatment assignment is based on a subjective assessment, as in some histo-pathological endpoints • Being asked the same question repeatedly, for example in a pain challenge model, could induce false differences • If a scoring method is subjective, then measures should be taken to minimize bias, for example, with the use of scripted questions, blinding or standard operating procedures. • Conclusions may be subject to many caveats if they are not based upon complete and representative data-sets 27/09/2012 PSI Biomarker SIG 8

Sources of missing data Biomarker studies can suffer from missing data issues, especially in exploratory situations: • • • Inaccessibility of tissues (e. g. lung cancer) Low consent rates for optional samples Lack of residual tissues in complete responders Poor quality of fixation in archival samples Patient drop-outs (lack of response or toxicity) Poor data handling procedures cf. clinical data Likely sources of missing data should be considered in advance so as to minimise potential implications and make appropriate assumptions 27/09/2012 PSI Biomarker SIG 9

Multiplicity when considering many markers • Range of new high-dimensional technologies – e. g. genetics, genomics, proteomics, metabonomics, NGS • Allow understanding in detail at a molecular level the processes of disease and response to treatment and identify biomarkers that can identify or predict these changes. • Multiplicity becomes a concern and requires new approaches to be adopted – e. g. False Discovery Rate, Permutation testing, Significance Analysis of Microarrays (SAM) • Pre-analysis filtering of variables can help • Want to emerge with both statistical significance scientifically relevant and meaningful effect. & – 2 D FDR 27/09/2012 PSI Biomarker SIG 10

Composites and Multivariate Analyses • Visualisation key – Principal Components Analysis (PCA), Clustering • Wide variety of supervised multivariate predictive modeling techniques are available – – Regression-based approaches e. g. Partial least Squares (PLS), Elastic Nets Proximity-based methods e. g. Nearest Neighbours Tree-based methods e. g. Random Forests, Gradient Boosting Distance-based approaches e. g. Support Vector Machines (SVM) • Not just “Black-Box”, Interpretation important – Importance scores • Study design is critical, as is visualization, understanding data quality and its impact on subsequent analyses. – MAQC-II 27/09/2012 PSI Biomarker SIG 11

Biomarkers for Personalised Healthcare • Personalized health care offers the potential to identify patients more likely to derive benefit from treatment and as such is of great interest to Pharmaceutical companies, Regulatory Authorities and Health care Providers • Predictive markers – marker by treatment interaction • Gives rise to new set of challenges : eg. Low power to robustly identify biomarkers in exploratory studies • Developing from a Biomarker to a Companion Diagnostic • Understanding and quantifying the factors that may impact the performance of the diagnostic • Identifying an optimal cut-off • Additional regulatory process 27/09/2012 PSI Biomarker SIG 12

Study design options • All-comers design Biomarker + – Subjects enrolled into groups, and retrospectively measured • Stratification design Control Biomarker Randomise – Biomarker status at screening used as a stratification factor in randomisation to ensure balance Biomarker + Experimental Biomarker - • Targeted design – Only biomarker positive patients recruited into study • Enriched design – Hybrid, recruiting a limited number of biomarker negative patients with a greater representation of biomarker positives • Adaptive design – Many options allowing testing and refinement of a biomarker 27/09/2012 PSI Biomarker SIG 13

Safety biomarkers preclinical & translation • Predictive safety biomarkers allow early detection of toxicity and assessment of human risk • Preclinical qualification including mechanistic understanding of the relationship between the biomarker and organ damage • Use known organ toxicants • Determination of organ toxicity in rat by qualified toxicological pathologist • Understanding properties of translation to man • Animal Model Framework (AMF) project is a collaborative effort combining pre-clinical and clinical data to determine operating characteristics and refine thresholds of Safety Pharmacology models 27/09/2012 PSI Biomarker SIG 14

Safety biomarkers - clinical • Clinical qualification less straightforward because generally verification of organ toxicity not possible • Utilise methods that can assess performance in absence of gold standard including Bayesian approaches • Currently lab parameters used to indicate kidney and liver injury, but poor performance – e. g. by time serum creatinine indicates drug induced kidney injury, a high degree of kidney function loss has already occurred • Public-private precompetitive partnerships established for searching for, validating and qualifying safety biomarkers for predicting drug-induced organ injury – Predictive Safety Testing Consortium (C-Path), SAFE-T (IMI) 27/09/2012 PSI Biomarker SIG 15

Biomarker qualification • Formal biomarker qualification process have been introduced: – FDA (qualification process for drug development tools) – EMA (qualification of novel methodologies and biomarkers) – Examples on EMA and FDA websites • These work alongside usual scientific advice routes and are a way to seek regulatory opinion on acceptability of a marker for a given use • This is not mandatory and so is usually not required, but may be an advantageous step, particularly for a marker with wide applicability (For example for collaborative groups seeking to develop a new endpoint, particularly for toxicity) • Diagnostic biomarkers would need to follow the in-vitro diagnostic regulatory processes 27/09/2012 PSI Biomarker SIG 16

Surrogate endpoints • Those outcomes for which treatment effects correlate well with those for an accepted clinical outcome at an individual and group level could potentially substitute for a recognized clinical endpoint • Should lead to the same decision being made as if the clinical outcome had been used • Examples exist (eg in diabetes or HIV), but a large body of evidence is required and this is not required for many purposes • Surrogacy is often an unrealistic target. • Statistical methods have been developed to examine the association between endpoints, but these can also be applied to other situations, for example when considering assurance when endpoints differ between studies 27/09/2012 PSI Biomarker SIG 17

Future challenges Issues to address in the future i How can the traditional clinical development program be reengineered, so allow for greater learning around a biomarker without delaying the drug program? Ii What study designs can be used to translate markers of drug toxicity into man given that the pre-clinical studies used for validation are not feasible to conduct in humans? iii Many technologies suffer from a ‘batch effect’. What pre-processing techniques can be developed to ensure we are measuring true biology rather than sample quality? iv New techniques have moved the degree of dimensionality by several orders of magnitude. How, both practically and analytically, can we cope with this deluge of data? v Effective ways, possibly Bayesian, could be developed to build existing pathway knowledge into data analyses. vi When identifying biomarkers for PHC, interaction analyses typically have low power, especially with many potential markers. How can such markers be reliably identified? vii As PHC becomes the processes for gaining approval of a drug, diagnostic and biomarker qualification may become more burdensome. How can this evidence be streamlined? 27/09/2012 PSI Biomarker SIG 18

Conclusions & Recommendations Key considerations for statisticians in biomarker development i Define clear objectives shaped by the potential future use of the marker - This will clarify what can reasonably be claimed based upon the research - Validation work and study designs should be “fit-for-purpose” relative to these aims ii Prospectively plan studies even if they are exploratory - Defining what represents “success” in advance will aid objective decision-making iii Consult expert colleagues to understand shape the endpoints definitions - Learn about distributions and pre-processing - Influence scoring methods so as to get maximum information iv Gather prior knowledge on sources of variability and causes of missing data - Plan ahead to avoid sample and data management issues rather than being reactive - Adjust missing data assumptions accordingly v Learn about a new marker using methodology studies or translational science - Estimates of variability or effect size can be valuable if reflective of situation of use 27/09/2012 PSI Biomarker SIG 19

Conclusions & Recommendations Key considerations for statisticians in biomarker development vi Analyse a biomarker using the appropriate methodology for the given endpoint type - As with other endpoints there is no universal approach vii Be alert when using large numbers of candidate biomarkers - Particular attention should be paid to issues of multiplicity and model validation viii Maintain a sceptical and questioning point of view to manage expectations - Consider results in relation to prior knowledge and study aims, avoiding extrapolation ix Consider how to add confidence to findings using other sources of information - Integrate biological information as well as numerical results x Remember that there is nothing mysterious about biomarkers - A consideration of key statistical principles can be instructive in most situations 27/09/2012 PSI Biomarker SIG 20

PSI Biomarker Special Interest Group Please get in touch if you would like to get involved! • Join the mailing list or linked-in group in • Help on the SIG committee • Review papers, training, discussion groups… • Attend one of our free meetings /or our conference sessions Biomarker Validation - Case Studies and Approaches Med. Immune, Cambridge, 10 October 2012 • Go to www. psiweb. org and click on “committees and sigs” in the menu to find our webpage with more information 27/09/2012 PSI Biomarker SIG 21